Skip to main content
SpringerLink
Log in

Hybrid Approach Combining Model-Based Method with the Technology of Machine Learning for Forecasting of Dangerous Weather Phenomena

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10408))

Included in the following conference series:

Abstract

The paper is a continuation of the works [1,2,3,4] where has been shown how the technologies of machine learning and online analytical processing (OLAP) could be used in conjunction with the numerical model of convective cloud for forecasting dangerous convective phenomena such as thunderstorm, heavy rainfall and hail. We study specifically the possibility of making predictions via a hybrid approach that combines the predictive numerical model of convective cloud with the modern methods of big data processing. We overview the existing examples of using of machine learning tools for weather forecasting and discuss the range of their applicability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Petrov, D.A., Stankova, E.N.: Use of consolidation technology for meteorological data processing. In: Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2014. LNCS, vol. 8579, pp. 440–451. Springer, Cham (2014). doi:10.1007/978-3-319-09144-0_30

    Google Scholar 

  2. Petrov, D.A., Stankova, E.N.: Integrated information system for verification of the models of convective clouds. In: Gervasi, O., Murgante, B., Misra, S., Gavrilova, M.L., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2015. LNCS, vol. 9158, pp. 321–330. Springer, Cham (2015). doi:10.1007/978-3-319-21410-8_25

    Chapter  Google Scholar 

  3. Stankova, E.N., Petrov, D.A.: Complex information system for organization of the input data of models of convective clouds. Appl. Math. Comput. Sci. Control Process. 10(3), 83–95 (2015). Vestnik of Saint-Petersburg University. (in Russian)

    Google Scholar 

  4. Stankova, E.N., Balakshiy, A.V., Petrov, D.A., Shorov, A.V., Korkhov, V.V.: Using technologies of OLAP and machine learning for validation of the numerical models of convective clouds. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O., Stankova, E., Wang, S. (eds.) ICCSA 2016. LNCS, vol. 9788, pp. 463–472. Springer, Cham (2016). doi:10.1007/978-3-319-42111-7_36

    Chapter  Google Scholar 

  5. Grover, A., Kapoor, A., Horvitz, E.: A Deep Hybrid Model for Weather Forecasting (2015). http://research.microsoft.com/en-us/um/people/horvitz/weather_hybrid_representation.pdf. Accessed 13 Aug 2016

  6. Meteum technology. https://yandex.ru/pogoda/meteum. Accessed 17 Jan 2017. (in Russian)

  7. The Weather Company Launches ‘Deep Thunder’ - the World’s Most Advanced Hyper-Local Weather Forecasting Model for Businesses https://www-03.ibm.com/press/us/en/pressrelease/49954.wss. Accessed 15 June 2016

  8. Using Amazon Machine Learning to Predict the Weather. https://arnesund.com/2015/05/31/using-amazon-machine-learning-to-predict-the-weather/. Accessed 20 Feb 2017

  9. Tutorial: Using Amazon ML to Predict Responses to a Marketing Offer. http://docs.aws.amazon.com/machine-learning/latest/dg/tutorial.html. Accessed 10 Mar 2017

  10. How Yandex predicts the weather. https://yandex.ru/company/technologies/meteum/. Accessed 17 Jan 2017. (in Russian)

  11. Weather Research and Forecasting Model. http://www.wrf-model.org/index.php. Accessed 7 Mar 2017

  12. Matrixnet. https://yandex.ru/company/technologies/matrixnet/. Accessed 26 Nov 2016. (in Russian)

  13. Grover, A., Kapoor, A., Horvitz, E.: A Deep Hybrid Model for Weather Forecasting (2015). http://research.microsoft.com/en-us/um/people/horvitz/weather_hybrid_representation.pdf. Accessed 13 Aug 2016

  14. Hastie, T., Tibshirani, R., Friedman, J.: The elements of Statistical Learning (2009). http://statweb.stanford.edu/~tibs/ElemStatLearn/. Accessed 15 Feb 2015

  15. Mitchell, T.: Machine Learning. Springer, Berlin (2009)

    MATH  Google Scholar 

  16. Codd, E.: Providing OLAP (on-line analytical processing) to user-analysts: an IT mandate, Technical report, E.F. Codd and Associates (1993)

    Google Scholar 

  17. Raba, N., Stankova, E.: Research of influence of compensating descending flow on cloud’s life cycle by means of 1.5-dimensional model with 2 cylinders. Proc. MGO 559, 192–209 (2009). (in Russian)

    Google Scholar 

  18. Raba, N., Stankova, E., Ampilova, N.: On investigation of parallelization effectiveness with the help of multi-core processors. Procedia Comput. Sci. 1(1), 2757–2762 (2010)

    Article  Google Scholar 

  19. Raba, N., Stankova, E.: On the possibilities of multi-core processor use for real-time forecast of dangerous convective phenomena. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, Bernady O. (eds.) ICCSA 2010. LNCS, vol. 6017, pp. 130–138. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12165-4_11

    Chapter  Google Scholar 

  20. Raba, N.O., Stankova, E.N.: On the Problem of Numerical Modeling of Dangerous Convective Phenomena: Possibilities of Real-Time Forecast with the Help of Multi-core Processors. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, Bernady O. (eds.) ICCSA 2011. LNCS, vol. 6786, pp. 633–642. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21934-4_51

    Chapter  Google Scholar 

  21. Raba, N.O., Stankova, E.N.: On the effectiveness of using the GPU for numerical solution of stochastic collection equation. In: Murgante, B., Misra, S., Carlini, M., Torre, Carmelo M., Nguyen, H.-Q., Taniar, D., Apduhan, Bernady O., Gervasi, O. (eds.) ICCSA 2013. LNCS, vol. 7975, pp. 248–258. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39640-3_18

    Chapter  Google Scholar 

  22. Scikit-learn: Machine Learning in Python. http://scikit-learn.org/

  23. Jedox. http://www.jedox.com/. Accessed 21 Nov 2015

  24. Gankevich, I., Korkhov, V., Balyan, S., Gaiduchok, V., Gushchanskiy, D., Tipikin, Y., Degtyarev, A., Bogdanov, A.: Constructing virtual private supercomputer using virtualization and cloud technologies. In: Murgante, B., Misra, S., Rocha, Ana Maria A.C., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, Bernady O., Gervasi, O. (eds.) ICCSA 2014. LNCS, vol. 8584, pp. 341–354. Springer, Cham (2014). doi:10.1007/978-3-319-09153-2_26

    Google Scholar 

  25. Gankevich, I., Gaiduchok, V., Gushchanskiy, D., Tipikin, Y., Korkhov, V., Degtyarev, A., Bogdanov, A., Zolotarev, V.: Virtual private supercomputer: design and evaluation. In: Computer Science and Information Technologies (CSIT), pp 1–6. IEEE Conference Publications (2013). doi:10.1109/CSITechnol.2013.6710358

Download references

Acknowledgment

This research was sponsored by the Russian Foundation for Basic Research under the projects: No. 16-07-01113.

Author information

Authors and Affiliations

  1. Saint-Petersburg State University, 7-9, Universitetskaya nab., St. Petersburg, 199034, Russia

    Elena N. Stankova & Evgeny V. Khvatkov

  2. Saint-Petersburg Electrotechnical University “LETI”, (SPbETU), ul. Professora Popova 5, St. Petersburg, 197376, Russia

    Elena N. Stankova, Irina A. Grechko & Yana N. Kachalkina

Authors
  1. Elena N. Stankova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Irina A. Grechko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yana N. Kachalkina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Evgeny V. Khvatkov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elena N. Stankova .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Stankova, E.N., Grechko, I.A., Kachalkina, Y.N., Khvatkov, E.V. (2017). Hybrid Approach Combining Model-Based Method with the Technology of Machine Learning for Forecasting of Dangerous Weather Phenomena. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10408. Springer, Cham. https://doi.org/10.1007/978-3-319-62404-4_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62404-4_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62403-7

  • Online ISBN: 978-3-319-62404-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation